Sudden cardiac death (SCD) is responsible for the death of up to 450,000 persons in the U.S. each year; similar incidences of SCD occur in other Western nations. The majority of these cases involve ventricular arrhythmias, not coronary occlusions and myocardial infarctions. SCD predominantly affects individuals in the prime of their lives, with most occurrences of life-threatening arrhythmia cases happening in the community (outside the hospital). Resuscitation is only attempted in a minority of patients, in part due to unavailability of defibrillation equipment and lack of knowledge and action by lay responders.
SCD is associated with common cardiac diseases, most notably heart failure, in which approximately 50% of patients die from fatal cardiac arrhythmias. Multiple factors in addition to reduced ejection fraction (EF) have been demonstrated to contribute to the risk for SCD after myocardial infarction. These include the presence of nonsustained ventricular tachycardia (NSVT), symptomatic heart failure (HF), and sustained monomorphic VT inducible by electrophysiologic cardiac testing.
However, fatal ventricular arrhythmias and SCD also frequently occur in young, otherwise healthy individuals without known structural heart disease. In structurally normal hearts the most common mechanism for induction and maintenance of ventricular tachycardia is abnormal automaticity. One form of abnormal automaticity, known as ‘triggered arrhythmias,’ is associated with aberrant release of Ca+2 that initiates delayed after-depolarizations (DADs). DADs, which can trigger fatal ventricular arrhythmias, are abnormal depolarizations in myocardial cells that occur after repolarization of a cardiac action potential. The molecular basis for abnormal Ca+2 release that causes DADs is, to date, incompletely understood.
In another form of abnormal automaticity in persons with an inherited, arrhythmogenic disorder known as “catecholaminergic polymorphic ventricular tachycardia”, physical exertion and emotional stress induce polymorphic ventricular tachycardias that lead to SCD in the absence of detectable structural heart disease.
On the cellular level, prolonged repolarization can result in early after-depolarizations (EADs), which are also arrhythmogenic. Prediction of SCD based on increased beat-to-beat T-wave lability during catecholamine-provocation has been examined previously.
Depending on the arrhythmogenic mechanism(s) that prevail at a given time for a particular individual, specific steps to prevent SCD can be selected and taken, notably for people who are in hospital at the time when a ventricular arrhythmia occurs. However, comprehensive prevention is hampered by multiple factors. Foremost among these is the present inability to identify predictive factors for the majority of patients at risk of SCD and to do so far enough in advance of the event that assessment and prevention/treatment services can be effectively implemented. The reason why this is so is that many who are at risk of SCD have no prior evidence of cardiac disease and are therefore not currently engaged with a health care system where assessment and prevention might take place.
Furthermore, even in patients at markedly elevated risk, amiodarone and other conventional, nonspecific anti-arrhythmic drug treatments have proven largely ineffective in preventing SCD particularly for ambulatory patients when the SCD event occurs outside of a hospital. Antiarrhythmic drugs have failed to prevent SCD in healthcare venues (ambulatory or acute care) owing substantially to the lack of timely dosing and dose-range adjustment of the medication in advance of the arrhythmia event; or to poor effectiveness on account of the nonspecificity or lack of relation of the prevailing arrhythmogenic mechanism and the selected drug's mechanism of action; or to adverse effects associated with the medication selected. Notwithstanding the historical reasons for lack of effectiveness to-date, were advance warnings and timely dosing implemented, the existing medications and new ones that are now under development may have a better chance of reducing SCD rates and improving survival.
Additionally, guidelines for medical device-based preventive therapies, such as implantable cardioverter-defibrillators (ICDs) or cardiac resynchronization therapy (CRT) for patients at elevated risk, are often not followed. In the case of ICDs, the reason is partly because physicians perceive that the majority of patients who receive these expensive, invasive therapies never experience life-threatening arrhythmias that would cause the implanted device to deliver cardioversion-defibrillation discharges.
It is because of these factors that an improved predictive-preventive method and system would be valuable, and in embodiments of such methods and systems, prediction classification or decision-support alert signals emitted by the system may be provided at logistically convenient times far enough in advance of a life-threatening arrhythmia's occurrence to allow for effective preventive intervention in a majority of cases. More over, embodiments of such a method and system can be inexpensive and suitable for a much larger population who are at moderate risk of SCD. Such a system could find use as a tool not only for surveillance and triaging the general medical-surgical patients in hospitals and other acute-care venues but also for ambulatory, free-living individuals such as athletes and the general elderly population who have one or more risk-factors for SCD.
Effective SCD preventive interventions vary and optimal selection and personalized tailoring of them can depend upon the patient's context, gender, age, heart conditions such as heart failure or coronary artery disease or left ventricular hypertrophy, ejection fraction, exercise inducibility of ventricular tachycardia, comorbid illnesses, concomitant medications, electrolyte abnormalities, family history of SCD, and other factors. In the case of a previously asymptomatic ambulatory person, effective preventive interventions may include consultation with the personal physician, presentation at a nearby emergency department for diagnostic assessment and close monitoring, and, optionally, prophylaxis with amiodarone or ranolazine loading or, in some situations, azimilide, dofetilide, or sotalol. In the case of a person with existing, known cardiac conditions, effective preventive interventions may include admission to hospital for observation and cardiac electrophysiology exams, provision of external pacing and resuscitation equipment at the ready, consideration for implantation of an ICD, or other alternatives.
Conventional cardiac rhythm measurements, such as R-R dispersion or abnormal QTc or QT dispersion (QTd), based on small samples of ECG waveforms acquired over short intervals (10 to 30 sec) have been shown to have inadequate statistical sensitivity and specificity for the purpose of predicting SCD.
When measurements rely upon apnea or disturbed respiratory patterns as the trigger or sentinel event for predicting incipient cardiac arrhythmias, the predictions are generally only relevant when the person is asleep. Additionally, the advance notice provided by disturbed respiratory pattern signals is so short (tens of seconds) as to preclude effective interventions to prevent the predicted arrhythmia or SCD occurrences.
Many prior art methods involve cumbersome, complex, expensive and/or invasive instrumentation, or require a skilled operator in attendance.
The most accurate predictive methods, such as paced electrogram fractionation analysis (PEFA), are highly invasive (involve placement of multiple catheters in the heart), are expensive, are not widely available, are only performable by subspecialty-trained cardiologists, and are only applicable to a small subset of patients who are already known to be at risk of SCD based on other attributes.
The methods involve expensive measurements, such as genomic or proteomic laboratory tests that are not widely available and that have a performance turnaround time of many hours or days before the results and prediction are available for use, such that the prediction or classification is not timely with respect to interventions aimed at preventing the predicted occurrences.
The methods are sensitive to, and may be compromised or entirely confounded by, individual variations in patient anatomy and physiology, such as cardiac axis deviation, pulmonary congestion, dyspnea, skeletal muscle signal artifact, patient movement and positioning, diurnal variations, etc.
The methods are sensitive to, and may be compromised or entirely confounded by, individual variations in operator positioning of electrodes or sensors on the patient's body or variations in the timing and method of acquiring the specimens or data that will enter into the prediction and classification.
Noninvasive electrocardiographic tools that have been approved by the U.S. Food and Drug Administration for identifying patients at risk for SCD (such as signal-averaged electrocardiogram (SAECG) and T-wave alternans (TWA) analysis) are relatively time-consuming to perform and, as such, are accessible to only a small subset of persons at-risk, mostly less healthy persons in acute-care settings, and even in this population exhibit a false-negative rate of more than 50%, in part because the interval of data capture is limited to the time of the exam.
QT interval dispersion (QTd) is still the most common and generally-available measure used to detect repolarization problems, but this too is generally only measured in a per-exam, discrete, “snapshot” fashion. While QTd is routinely measured using manual ECG methods, software algorithms to automatically perform the measurement are available, and these could be implemented in a continuous fashion instead of discrete, point-in-time snapshots. However, these prior art software algorithms suffer from the same signal-processing problems and artifacts that arise in measuring the QT interval generally. The most common alternative approach has been to devise measurements that consider the T-wave's morphology, or overall shape. For example, one approach used the width of root-mean square curves of T-waves and found much higher correlation with the repolarization dispersion than was found for QTd. Another approach compared several novel computational measures of T-wave morphology with QTd. But short-term measurement of QTd was found to be an inadequate predictor of SCD. That approach found that a measure named total cosine R-to-T T-wave morphology dispersion was useful in assessing malignant arrhythmia risk in post-myocardial infarction patients. Machine learning techniques have also been applied to various aspects of the repolarization-dispersion problem. One approach used principal component analysis (PCA) to detect repolarization abnormalities and found the method outperformed QTd for men, and exhibited predictive performance equivalent to QTd for women. Wavelet analysis, Gaussian mesa function analysis, and machine learning approaches have been used for ECG delineation, QT interval measurement, and rhythm classification. Machine learning has also been applied to predict the occurrence of drug-induced Torsades de Pointes.
But the prior art is deficient of teachings regarding examining mathematical stability properties of the measured variables. Nor has the prior art made use of continuous realtime measurements over long periods of many hours, for instance. Despite the existence of Holter monitor type ECG recording equipment for approximately 40 years, the analysis of long-timeseries Holter data is traditionally restricted to abnormal beats or rhythms, and calculation and study of RRd(t), QTd(t), and other parameters are never performed. Only small selected portions of the recorded data are subjected to detailed analysis, and the rest are typically discarded unexamined or ignored.